Mean-field dynamics of a random neural network with noise

Abstract

We consider a network of randomly coupled rate-based neurons influenced by external and internal noise. We derive a second-order stochastic mean-field model for the network dynamics and use it to analyze the stability and bifurcations in the thermodynamic limit, as well as to study the fluctuations due to the finite-size effect. It is demonstrated that the two types of noise have substantially different impact on the network dynamics. While both sources of noise give rise to stochastic fluctuations in the case of the finite-size network, only the external noise affects the stationary activity levels of the network in the thermodynamic limit.We compare the theoretical predictions with the direct simulation results and show that they agree for large enough network sizes and for parameter domains sufficiently away from bifurcations.

References in corpus (8)

• Auto and crosscorrelograms for the spike response of LIF neurons with slow synapses
• Balanced networks of spiking neurons with spatially dependent recurrent connections
• Noise-induced cooperative dynamics and its control in coupled neuron models
• Mean field approximation of two coupled populations of excitable units
• Generalized Rate-Code Model for Neuron Ensembles with Finite Populations
• Population rate codes carried by mean, fluctuation and synchrony of neuronal firings
• Persistence and failure of mean-field approximations adapted to a class of systems of delay-coupled excitable units
• A Generalized Rate Model for Neuronal Ensembles

Cited by in corpus (3)

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